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Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach

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  • Yongjun Zhu
  • Min Song
  • Erjia Yan

Abstract

In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications.

Suggested Citation

  • Yongjun Zhu & Min Song & Erjia Yan, 2016. "Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
    • Handle: RePEc:plo:pone00:0156091
    DOI: 10.1371/journal.pone.0156091
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    References listed on IDEAS

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    1. Min Song & Su Yeon Kim, 2013. "Detecting the knowledge structure of bioinformatics by mining full-text collections," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 183-201, July.
    2. Yan, Erjia & Zhu, Yongjun, 2015. "Identifying entities from scientific publications: A comparison of vocabulary- and model-based methods," Journal of Informetrics, Elsevier, vol. 9(3), pages 455-465.
    3. Editors The, 2009. "Content," Basic Income Studies, De Gruyter, vol. 4(1), pages 1-1, August.
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    Cited by:

    1. Qikai Cheng & Jiamin Wang & Wei Lu & Yong Huang & Yi Bu, 2020. "Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1923-1943, September.
    2. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).

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